Provision and analysis of key R&I indicators. Pooled policy briefs B & C, Diversity and specialisation in intellectual property, B. Brief on EUIPO trademarks & designs, C. Brief on EPO patents.
Date: 2021-10-08
Institutions: Directorate-General for Research and Innovation
Subjects: EU growth strategy, EU research policy, designs and models, innovation, patent, performance measurement, policymaking, report, trade mark

Provision and analysis of key 
R&I indicators 

Pooled policy briefs B & C – Diversity and specialisation 
in intellectual property 

 B. Brief on EUIPO trademarks & designs 

C. Brief on EPO patents

David Campbell, Bamini Jayabalasingham and Christian Lefebvre 
April 2021 

 
 
 
 
 
 
 
 
 
 
Provision  and  analysis  of  key  R&I  indicators:  Pooled  policy  briefs  B  &  C  –  Diversity  and 
specialisation in intellectual property 

European Commission 
Directorate-General for Research and Innovation 
Directorate G — Common Policy Centre 
Unit G.1 — Common R&I Strategy & Foresight Service 
Contact   Athina Karvounaraki 

Email  

Tiago Pereira 
Alexandr Hobza 
RTD-STATISTICS@ec.europa.eu 
Athina.KARVOUNARAKI@ec.europa.eu 
Tiago.PEREIRA1@ec.europa.eu   
RTD-PUBLICATIONS@ec.europa.eu 

European Commission 
B-1049 Brussels 

Manuscript completed in April 2021.  

This document has been prepared for the European Commission, however it reflects the views only of the authors, and the 
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More information on the European Union is available on the internet (http://europa.eu). 

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EUROPEAN COMMISSION 

Provision and analysis of key 
R&I indicators 

Pooled policy briefs B & C – Diversity and 
specialisation in intellectual property  

B. Brief on EUIPO trademarks & designs 

C. Brief on EPO patents 

Prepared by 

2021  

Directorate-General for Research and Innovation 

EN 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
IP type 

2.1 
2.2  Definitions of indicators used in this study 

Table of Contents 
1  Study concept and approach ........................................................................................ 3 
2  Methods ..................................................................................................................... 5 
5 
7 
3  Policy brief on EUIPO trademark and community design applications ................................. 8 
8 
10 
15 
4  Policy brief on EPO patent applications........................................................................... 19 
19 
5  Conclusion ................................................................................................................. 23 

3.1  Relationship between output size and diversification 
3.2  Results for trademarks 
3.3  Results for design applications 

4.1  Results for patents 

Tables 
Table 1  Diversity  in  the  specialisation  pattern  and  diversity  index  of  EU  countries  and 
international  comparators  based  on  the  distribution,  relative  to  the  world,  of  their 
trademark applications across Nice classes, 2010–2019 .......................................13 

Table 2 

Trends in specialisation pattern,  across Nice  classes (trademark applications),  of EU 
countries relative to international comparators, 2013–2019 .................................14 

Table 3  Diversity in the specialisation pattern of EU countries and comparators based on the 
distribution,  relative  to  the  world,  of  their  community  design  applications  across 
Locarno classes, 2010–2019 .............................................................................17 

Table 4 

Trends  in  specialisation  pattern,  across  Locarno  classes  (community  design 
applications), of EU countries relative to international comparators, 2013–2019 .....18 

Table 5  Diversity in the specialisation pattern of EU countries and comparators based on the 
distribution,  relative  to  the  world,  of  their  EPO  patent  applications  across  NACE 
sectors, 2010–2017 ........................................................................................21 

Table 6 

Trends  in  specialisation  pattern,  across  NACE  sectors  (patent  applications),  of  EU 
countries relative to international comparators, 2013–2019 .................................22 

Figures 

Figure 1  Relationship  between  a  country’s  number  of  IP  applications  (top  part  = 
untransformed; bottom part = log transformed) and the diversity of its specialisation 
pattern for trademarks and designs (2017–2019) ...............................................10 

Figure 2  Relationship  between  a  country’s  number  of  IP  applications  and  the  diversity  of  its 
specialisation pattern for patents (2015–2017) ...................................................19 

2 

 
 
 
 
1 

Study concept and approach 

This pooled policy brief on diversity and specialisation in intellectual property (IP) is part 
of a broader three-year project on ‘Key indicators in research and innovation’ (KIRI). This 
project was intended, in part, to feed the EU monitoring system for the Innovation Union 
initiative,  one  of  the  seven  flagship  initiatives  of  the  Europe  2020  strategy  designed  to 
boost  ‘smart,  sustainable  and  inclusive  growth’1.  In  particular,  the  collected  data 
supported key publications by the European Commission that were designed to track R&I 
performance  at  national  and  regional  level  in  Europe2,  as  well  as  progress  towards  the 
European  Research  Area  (ERA)  over  the  2014–2020  period.  The  ERA  –  ‘a  single, 
borderless market for research, innovation and technology across the EU’3 – is central to 
Europe’s strategy for productivity growth. 

The  latest  ERA  communication,  titled  ‘A  new  European  Research  Area’,  emphasises, 
among  other  things:  (1)  the  smart  directionality  of  R&I  investments  to  prioritise  the 
green  and  digital  transition  in  support  of,  for  example,  the  Sustainable  Development 
Goals, and (2) the translation of research results to innovation and the market in support 
of  Europe’s  recovery4.  In  this  context,  the  production  of  data  on  various  types  of  IPs, 
along with data on scientific publications, is useful in monitoring trends in the impacts of 
investment directionality along the R&I continuum (e.g. Are increases in R&I investment 
for the green and digital transition impacting the specialisation of EU countries/regions as 
measured from their R&I outputs? Are some countries/regions more focused/effective in 
emphasising this transition?). 

In the context of the KIRI project, the European Commission gathered, for the first time, 
data  on  two  additional  types  of  IP  –  trademarks  and  community  designs  –  as  a 
complement to patent data. IP  refers to the protection, from unlawful use,  of intangible 
assets  resulting from innovation (or creations  of the mind). There are different types  of 
IP  depending  on  the  nature  of  such  intangible  assets  (e.g.  inventions,  art,  literature, 
designs, symbols and names used in commerce). 

The trademark and design data produced as part of the KIRI project were not produced 
according  to  bespoke  data  sets  specifically  designed  to  capture  trends  in  the 
specialisation index (SI) of countries in thematic areas of relevance to the EU’s green and 
digital  transition.  Instead,  they  were  produced  per  standardised  classification  schemes 
(as  depicted  in  the  study’s  analytical  report  on  trademarks  and  designs).  Such 
classification schemes lack the granularity that would be required to monitor the impacts 
of  smart  directionality  under  the  new  ERA  (2021–2027).  Still,  the  data  produced  as 
part  of  the  KIRI  project  can  already  be  used  to  inform  the  monitoring 
requirements  (in  terms  of  data  sources  and  analytical  strategy)  towards 
assessing the impacts of investment directionality under the new ERA. 

1  European  Commission.  (2011).  Horizon  2020  –  The  Framework  Programme  for  Research  and 
http://eur-lex.europa.eu/legal-
final.  Retrieved 

Innovation.  COM(2011)  808 
content/EN/TXT/PDF/?uri=CELEX:52011DC0808&from=EN  

from 

2 https://ec.europa.eu/info/strategy/european-semester, 

http://ec.europa.eu/growth/industry/innovation/facts-figures/scoreboards_en, 
https://ec.europa.eu/growth/industry/innovation/facts-figures/regional_en  

3 https://ec.europa.eu/info/research-and-innovation/strategy/era_en  

4 European Commission. (2020). Communication from the Commission to the European Parliament, 
the Council, the European Economic and Social Committee and the Committee of the regions. A 
new ERA for Research and Innovation. SWD(2020) 214 final. European Commission. Retrieved 
from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2020%3A628%3AFIN  

3 

 
                                                 
The specialisation index (SI) measures the share of a country’s output devoted to 
an innovation area relative to the global average (the world) in a reference database. 
A  score  above  1  indicates  that  a  country  is  specialised  in  a  given  area,  producing  a 
greater fraction of its total output in this area than the world. 

The  trademark  and  design  data  produced  as  part  of  the  KIRI  project  and  briefly 
introduced  in  the  study’s  corresponding  analytical  report  are  based  on  a  European  data 
source (i.e. European Union Intellectual Property Office, EUIPO), which might impact the 
international comparability of specialisation data. Additionally, based on several years of 
experience analysing the specialisation patterns of countries using an SI, Science-Metrix 
is well aware of the dangers of comparing such patterns across entities of varying sizes, 
especially  on  an  international  scale  if  the  source  of  data  is  more  local  (e.g.  EUIPO).  In 
such contexts, large countries (or country aggregates) may contribute a significant share 
of  the  world  output  (e.g.  one  third  or  more)  used  in  measuring  the  specialisation  of 
countries per innovation area. As a result, their specialisation cannot easily diverge from 
the  world  reference  in  any  area  since  they  define  it  to  a  large  extent.  In  turn,  large 
countries (or country aggregates) are less prone to diverge from the world level, leading 
to  less  polarised  (or  more  diversified)  specialisation  patterns.  Plus,  it  naturally  follows 
that countries (or regions), as they grow, will diversify into a broader range of activities 
building on their pre-existing strengths (i.e. their specialties)5. As a result, more extreme 
SIs  (or  concentration  of  output  in  a  given  area)  are  to  be  expected  for  smaller  rather 
than larger countries. 

To  investigate  this  potential  risk  in  the  monitoring  context  described  above  for  the  new 
ERA, a policy brief on trademarks and designs (Section 3) first examined the dependence 
of  the  diversity  of  specialisation  patterns  on  the  output  size  of  countries.  This  was 
achieved  using  data  on  the  specialisation  (or  diversity)  of  Member  States  (and  regional 
aggregates  such  as  the  EU-27  and  ERA)  and  key  international  comparators  (i.e.  the 
United Kingdom, Australia, Brazil, Canada, China, India, Japan, South Korea, the Russian 
Federation,  South  Africa  and  the  United  States)  and  using  standardised  classification 
schemes; we are assuming the findings would hold for a customised scheme adapted to 
the  new  ERA.  This  analysis  attempted  to  address  a  few  questions  whose 
responses  were  then  used  in  formulating  recommendations  for  the  future 
monitoring  of  the  impacts  of  investment  directionality  under  the  new  ERA.  For 
example,  should countries be ranked based  on the SI?  If  so,  should it be within certain 
size  limits?  Should  the  SI  be  strictly  used  to  follow  the  progress  of  individual  countries 
relative to their respective baseline value and target? Can regional databases be used in 
international comparisons (e.g. EUIPO)? In a second phase, and acknowledging the 
above  limitations,  the  policy  brief  aimed  to  extract  general  tendencies  in  the 
specialisation  pattern  of  the  selected  countries  for  trademarks  and  community 
designs.  The  same  approach  was  then  applied  to  a  second  policy  brief  on  patents 
(Section 4). 

Before  diving  into  the  presentation  of  results  for  these  two  policy  briefs,  the  methods 
section  below  presents  each  of  the  three  types  of  IP  protection  they  cover,  along  with 
their  respective  data  source,  classification  scheme  and  period  of  analysis  (Section  2.1). 
Section 2.2 presents the study’s indicators. 

5 Balland, P. A., Boschma, R., Crespo, J., & Rigby, D. L. (2019). Smart specialization policy in the 
European  Union:  relatedness,  knowledge  complexity  and  regional  diversification.  Regional 
Studies, 53(9), pp. 1252–1268. doi:10.1080/00343404.2018.1437900. 

4 

 
 
                                                 
2  Methods 

2.1 

IP type 

Given  the  focus  of  the  study  on  the  EU-27,  and  more  broadly  the  ERA,  the  EUIPO 
database  was  used  because  it  covers  the  European  market  for  trademarks  and  design 
applications. For the same reason, the PATSTAT database was used to look specifically at 
data for the European Patent Office (EPO). The emphasis placed on the European market 
creates  a  home-bias  advantage  in  favour  of  the  EU-27  and  the  ERA  relative  to 
international  comparators;  this  has  been  accounted  for  in  discussing  the  briefs’  results. 
For trademarks and designs, the period of analyses covered 2010 to 2019. Due to delays 
in the publication of patent applications, the patent analysis covered 2010 to 2017. 

2.1.1.  TRADEMARKS 

Trademarks  are  a  form  of  IP  that  protect  symbols  or  signs  used  to  identify  products. 
Trademarks  can  be  registered  at  national  (e.g.  United  States  Patent  and  Trademark 
Office) or regional offices (e.g. EUIPO) to benefit from protection within their jurisdiction 
and  can  also  be  extended  across  jurisdictions  through  the  Madrid  System  for  the 
international registration of marks. The EUIPO provides for 11 types of trademarks6: 

1.  Word – consisting of exclusively words or letters 

2.  Figurative – consisting of non-standard characters, style or layout 

3.  Shape – consisting of three-dimensional shape (e.g. packaging) 

4.  Position – the manner in which a mark is placed or affixed to an item 

5.  Pattern – consisting of a set of elements repeating regularly 

6.  Colour (single) – consisting of a single colour 

7.  Colour  (combination)  –  consisting  exclusively  of  a  combination  of  colours  (without 

contours) 

8.  Sound – consisting of a sound or combination of sounds 

9.  Motion – the motion or changing of position of a mark 

10.  Multimedia – consisting of a combination of images and sounds 

11.  Hologram – consisting of elements with holographic characteristics 

Other  types  of  trademarks  are  listed  by  the  EUIPO;  however,  those  noted  above 
represent  those  included  in  the  EUIPO  database.  There  are  a  further  three  kinds  of 
trademarks recognised by the EUIPO: 

1. 

Individual  –  a  mark  that  differentiates  a  good  or  service  of  an  entity  from  that  of 
another 

2.  Collective – a mark that differentiates a good or service of a group of companies or 
an association from those of another (e.g. an association of regional wine producers) 

3.  EU  Certification  –  a  mark  certifying  goods  or  services  as  compliant  with  EU 

certification requirements (e.g. a certification of food-safe packaging) 

6 For more details see: https://euipo.europa.eu/ohimportal/en/trade-mark-definition  

5 

 
                                                 
The thematic scope of trademarks can be assessed through their categorisation into one 
or  more  classes  based  on  the  Nice  Classification7.  The  Nice  Classification  system  is 
specified  by  the  World  Intellectual  Property  Organization  (WIPO)  and  was  created  in 
1957. Updated every five years, the Nice Classification is widely used internationally for 
the classification of trademarks and provides a unified system for international trademark 
applications. The Nice Classification contains 45 classes, with 34 for goods and a further 
11  for  services  (see  Appendix  A).  As  it  is  possible  for  an  individual  trademark  to  be 
classified under multiple Nice classes, the total number of trademarks across classes may 
exceed the total number of trademarks in the database. 

In  this  study,  the  thematic  diversity  of  selected  countries  based  on  trademarks  was 
assessed using the Nice classes. 

2.1.2.  COMMUNITY DESIGNS 

Community designs refer to ‘The appearance of the whole or a part of a product resulting 
from  the  features  of,  in  particular,  the  lines,  contours,  colours,  shape,  texture  and/or 
materials of the product itself and/or its ornamentation’8. In practical terms, designs can 
refer  to  logos,  typefaces,  maps  or  packaging,  among  other  items.  Data  on  design 
applications are available in the EUIPO database as of 2003. 

All  EUIPO  community  designs  are  classified  according  to  the  Locarno  Classification 
system  for  industrial  designs.  Like  the  Nice  Classification  used  for  trademarks,  the 
Locarno Classification is specified by the WIPO and is used internationally to facilitate the 
registration  of  industrial  designs.  The  current  iteration  of  the  Locarno  Classification 
contains 32 classes (see Appendix B). 

In  this  study,  the  thematic  diversity  of  selected  countries  based  on  community  designs 
was assessed using the Locarno classes. 

2.1.3.  PATENTS 

Patents  are  one  form  of  IP  protection  for  new/improved  technologies,  processes  or 
products.  Patents  contain  valuable  information  that  may  be  analysed  to  quantify  the 
innovation  activities  of  various  entities  such  as  countries,  regions  and  institutions  (e.g. 
companies) applying for patents to protect their IP. 

PATSTAT contains rich patent information for a wide range of patent offices in the world, 
including  the  EPO  whose  patent  data  were  used  in  this  study.  Numerous  classification 
schemes  are  available  to  categorise  patents  by  thematic  area.  Many  of  these  schemes 
are  readily  available  in  PATSTAT,  such  as  the  International  Patent  Classification,  the 
Cooperative  Patent  Classification  and  the  Statistical  Classification  of  Economic  Activities 
in the European Community (NACE). 

Here,  the  thematic  diversity  of  selected  countries  based  on  EPO  patents  was  assessed 
using  the  NACE  classes  (using  the  tls229_appln_nace2  table  in  PATSTAT)  as  defined 
below in Appendix C (18 categories). 

7 http://www.wipo.int/classifications/nice/en/  

8 Article 3 of the European Commission regulation on community designs: 

https://euipo.europa.eu/tunnel-
web/secure/webdav/guest/document_library/contentPdfs/designs/design_definition/62002_cv_
en.pdf  

6 

 
                                                 
2.2  Definitions of indicators used in this study 

Output  size  (i.e.  number  of  trademarks,  community  designs  or  patent 
applications): All IP indicators were computed using fractional counting, whereby each 
applicant  on  a  trademark,  community  design  or  patent  application  is  credited  an  equal 
fraction of the application. The applicants’ fractions are attributed to countries based on 
the applicants’ addresses. For trademarks and designs, the applicants are the owners of 
the  IP  and  can  be  a  physical  person,  an  organisation  (most  often  a  company)  or  both. 
Data for trademarks and designs could have been computed per the  application date  or 
the  registration  date.  The  application  date  was  used  as  it  informs  on  when  the  IP  was 
conceived. This choice was also justified by the fact that by counting per the application 
date, items that were  submitted but not accepted were  also counted.  This makes sense 
as  it  stands  to  reason  that  if  an  individual  or  company  embarks  on  the  process  of 
protecting an item, it does represent some form of innovative activity for that entity.  

For patents, data can be produced based on the geographic location of the assignees (i.e. 
the  owner  of  the  IP)  or  the  inventors.  Since  the  focus  is  on  the  inventiveness  of 
countries,  the  data  presented  in  this  report  are  based  on  the  physical  location  of  the 
persons who created the inventions (most often employees of the IP owners9) for which 
protection  was  sought.  For  the  same  reasons  outlined  for  trademarks  and  designs,  the 
patent data are based on applications rather than issued patents. 

Specialisation  index:  The  SI  measures  the  share  of  an  entity’s  output  devoted  to  an 
innovation  area  relative  to  the  global  average  (i.e.  to  the  EUIPO’s  full  content  for 
trademarks  and  designs,  and  to  EPO’s  full  content  for  patents).  For  trademarks, 
innovation areas are based on Nice classes (see Appendix A). For designs, they are based 
on  Locarno  classes  (Appendix  B).  For  patents,  they  are  based  on  NACE  Rev  2.0  codes 
(Appendix C). 

The SI reference value is 1 (i.e. the world level is always equal to 1 in all areas). An SI 
above  1  shows  that  an  entity  is  specialised  in  the  corresponding  area  because  it 
proportionately produces more output (trademarks, designs or patents) than the average 
in this area. An SI below 1 shows the opposite. For example, if 20 % of a given country’s 
design  applications  are  in  class  6  (furnishing),  but  at  the  global  level  only  15  %  of 
applications are in this class, then the country is said to be specialised in furnishing (SI = 
0.2/0.15  =  1.33),  producing  33  %  more  output  in  that  area  than  is  normally  the  case 
elsewhere in the world.  

The SI is a zero-sum game because it is measured as a proportion of total output. If the 
proportion of an  entity’s output in one area increases, there must be relative decreases 
elsewhere. Note that the SI does not reflect the total output a country produces, because 
it  is  a  relative  measure.  A  country  may  produce  a  greater  share  of  its  output  in  field  A 
than  field  B,  but  if  its  share  in  field  A  is  smaller  than  that  at  world  level,  it  is  not 
considered specialised in field A, whereas the opposite could be the case for field B (e.g. 
country: field A = 10 %, field B = 5 %; world: field A = 20 %, field B = 5 %; SI field A = 
0.5, SI field B = 1). 

Because the  EUIPO and EPO present a bias in favour  of  European countries, differences 
between  EU-27  (or  ERA  countries)  and  international  comparators  must  be  interpreted 
cautiously.  For  instance,  EU-27  and  ERA  countries  collectively  account  for  60 %  and  70 
%  of  EUIPO  trademark  and  design  applications,  respectively.  They  also  respectively 
account for 37 % and 45 % of EPO patent applications. This means that in the aggregate 
they  are  bound  to  more  closely  follow  the  world’s  specialisation  pattern,  which  is,  by 
definition, the most diverse (world SI equals 1 in all areas, see below). 

9  The  ownership  of  an  invention  is  often  assigned  to  the  headquarters  of  a  company  even  if  the 

R&D has been performed in a different location. 

7 

 
                                                 
Diversity  of  specialisation  pattern:  An  unequal  distribution  of  SI  values,  with  both 
high and low scores across areas for a given country, highlights a polarised specialisation 
pattern  with  strong  relative  emphasis  on  one  or  many  focal  areas.  Alternatively,  a 
uniform  distribution  of  SI  values  across  areas  highlights  a  diversified  specialisation 
pattern  whereby  outputs  are  present  in  all  areas  without  special  emphasis  on  any  area 
relative to the world. 

The diversity of an  entity’s  specialisation pattern was thus  computed  by subtracting the 
Gini coefficient of its SI scores (0  = uniform distribution; 1 means an  entity is active in 
only one area) from 1: 

𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 𝑜𝑓 𝑠𝑝𝑒𝑐𝑖𝑎𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛 𝑝𝑎𝑡𝑡𝑒𝑟𝑛 = 1 − 𝐺𝑖𝑛𝑖 

Thus,  the  lower  the  diversity  of  specialisation  pattern,  the  higher  the  relative 
concentration of an entity’s efforts in one or a few areas (a combination of high and low 
SI scores), while the maximum diversity of the specialisation pattern of an entity would 
be 1, representing a uniform distribution across research areas (i.e. SI = 1 in all areas as 
for  the  world).  A  high  diversity  shows  that  a  country  closely  mirrors  the  global  (world) 
pattern  (no  entity  can  be  more  diversified  than  the  world),  whereas  a  low  score  shows 
that  a  country  departs  more  substantially  from  that  overall  pattern  (it  is  specialised 
rather than diversified relative to the world). 

Due to the home bias advantage of EU-27 and ERA countries in EUIPO and the EPO, both 
the  EU-27  and  ERA,  in  the  aggregate,  exhibit  a  rather  uniform  distribution  of  SI  scores 
(always  close  to  1)  across  areas;  in  other  words,  to  a  large  extent  they  set  the  world 
baseline  against  which  specialisation  is  measured  and  are  unsurprisingly  the  most 
diversified entities (apart from the world). For this reason, it is irrelevant to compare the 
diversity of the EU-27 and ERA in the aggregate to international comparators. It is more 
relevant  to  compare  the  diversity  of  countries  of  a  similar  output  size,  within  the 
EUIPO/EPO,  admitting  that  large  European  countries  (e.g.  France,  Germany,  the  United 
Kingdom)  are  possibly  advantaged  relative  to  countries  such  as  China,  Japan,  South 
Korea and the United States. For example, the US and China are only a fifth of the size of 
the EU-27 based on EUIPO trademarks (see Table 1, Section 3.1). 

3 

Policy brief on EUIPO trademark and community design applications 

3.1  Relationship between output size and diversification 

Diversity is expected to increase with the output size of the entities being compared; the 
more output a country has, the more likely it is to appear in diverse areas. In fact, there 
appears to be a logarithmic relationship between a country’s number of applications and 
the  diversity  of  its  specialisation  pattern  for  trademarks  and  designs.  The  top  part  of 
Figure  1  shows  the  x-axis  (number  of  applications)  untransformed  to  reveal  the 
logarithmic  shape  of  the  relationship  for  trademarks  and  designs.  The  bottom  part  of 
Figure  1  shows  the  same  data,  but  this  time  with  the  x-axis  log  transformed.  In  that 
case,  the  relationships  become  linear,  which  is  per  expectation  for  logarithmic 
relationships.  In  fact,  the  strength  of  these  relationships  is  much  higher  when  fitting  a 
linear  regression  on  the  log  transformed  data  (trademarks:  R2  =  0.63;  designs:  R2  = 
0.72,  Figure  1)  than  when  fitting  a  linear  regression  on  the  non-transformed  data 
(trademarks: R2 = 0.15; designs: R2 = 0.44, data not shown). The analysis was repeated 
for  several  three-year  periods  from  2010  to  2019,  showing  that  the  strength  of  the 
logarithmic relationship was stable (data not shown). 

The  diversity  of  a  country’s  specialisation  pattern  is  dependent  on  the  size  of  its 
output for trademarks and designs. The relationship between output size and diversity 
is  logarithmic  and  its  non-linear  character  should  be  accounted  for  in  comparing 
diversity across countries of different sizes. 

8 

 
 
Even  though  the  strengths  of  these  relationships  are  not  very  strong,  they  clearly 
demonstrate the importance of accounting, in a non-linear way, for the respective size of 
countries in comparing the diversity of their specialisation patterns (the same holds true 
for  the  SI  in  a  given  area).  Diversity  cannot  be  directly  divided  by  population  size  as  is 
often done with publication or patent counts. First, such a division would assume a linear 
link  between  diversity  and  size,  which  is  not  the  case.  Second,  diversity  is  bounded 
between 0 and 1, whereas population size is not bounded. 

To  facilitate  comparing  the  diversity  of  specialisation  patterns  across  countries,  an 
alternative  indicator  called  the  diversity  index  is  proposed.  When  the  number  of 
applications  (x-axis) is  log  transformed,  logarithmic  relationships  can  be  analysed  using 
linear  regression  models.  Therefore,  the  approach  used  to  compute  the  diversity  index 
consists  in  performing a  linear  regression  analysis  between  the  natural logarithm  of  the 
number  of  applications  and  the  diversity  index  at  a  specific  aggregation  level  (e.g. 
countries  in  a  given  period  of  time)  to  estimate  the  constants  (m  and  b)  of  the 
logarithmic relationship: 

Where, 

𝐷𝐼𝑉exp(𝐴) = 𝑚 ln(𝐴) + 𝑏 

DIVexp = the expected diversity of a country based on the regression model; and 

A = the countries’ number of applications. 

The  diversity  index  is  then  simply  the  ratio  of  observed  (DIVobs)  to  expected  (DIVexp) 
diversity. This way, the diversity index also accounts for the non-linear character  of the 
association between a country’s output size and its diversity. The diversity index ranges 
from 0 (i.e. a highly specialised country active in only one area) to infinity (diversity well 
above expectation for a given country size). A score above 1 indicates that a country is 
more diversified than expected per the size of its output, whereas a score below 1 means 
the  opposite.  Another  alternative  would  be  to  compare  the  diversity  of  countries  of  a 
similar size by number of applications. 

Trends  in  the  diversity  index  were  quantified  using  the  compound  annual  growth  rate 
(CAGR)  applied  to  three-year  running  periods10.  The  CAGR  in  the  diversity  index 
measured by how much a country’s or country grouping’s score changed every year, on 
average, moving from a starting value (SV) in a starting period to an ending value (EV) 
in  an  ending  period  (CAGR  =  (SV/EV)(1/(no.  of  running  periods  -  1))-1).  The  CAGR  captures  the 
average yearly rate of change moving across overlapping three-year running periods. 

10 That is, overlapping three-year periods from 2010–2012, 2011–2013…to 2017–2019. This makes 

it possible to smooth trends. 

9 

 
 
 
                                                 
Figure 1 

Relationship  between  a  country’s  number  of  IP  applications  (top 
part  =  untransformed;  bottom  part  =  log  transformed)  and  the 
diversity  of  its  specialisation  pattern  for  trademarks  and  designs 
(2017–2019) 

Note: 

A country’s diversity of specialisation pattern was characterised using Nice classes for trademarks and across Locarno 

classes for community designs. The countries used in this analysis include ERA countries and the selected international 
comparators. 
Source:  Produced by Science-Metrix using data from the EUIPO and PATSTAT databases 

3.2  Results for trademarks 

Table 1 shows diversity metrics related to trademark applications at the country level as 
well  as  for  country  groupings  in  Europe  (EU-27  and  ERA).  Note  that  due  to  their  sheer 
size  and  home-bias  advantage  in  representation  in  the  EUIPO  data,  the  EU-27  and  ERA 
account  for  most  of  the  world  output  for  trademarks,  scoring  well  above  individual 
countries  (including  international  comparators)  for  their  number  of  trademarks.  By 
construction,  they  are  thus  near  the  world  level in  specialisation in  all  classes,  meaning 
they have the most diverse (or least polarised) specialisation patterns across classes. It 
is  thus  irrelevant  to  compare  Europe  in  the  aggregate  (i.e.  EU-27  and/or  ERA)  to 
individual countries using the diversity of specialisation pattern. What is most relevant is 
contrasting the diversity index of individual countries – that is, comparing the diversity of 
their  specialisation  pattern  while  accounting  for  the  size  of  their  output  (by  number  of 
trademark  applications)  –  or  alternatively,  directly  comparing  the  diversity  of 
specialisation  patterns  across  countries  of  a  similar  size.  Note  that  country  aggregates 
were  not  included  in  estimating  the  parameters  of  the  relationships  used  in  computing 
the diversity index of entities, this to safeguard the independence of observations on the 
number of applications when performing the above regression analysis in computing the 
diversity  index.  Accordingly,  the  diversity  index  of  the  EU-27  and  ERA  could,  in  theory, 
be  compared  to  those  of  individual  European  countries  and  their  international 
comparators (further details below). 

10 

 
 
 
 
To  illustrate  the  use  of  the  diversity  index,  let  us  first  look  at  an  example  within  EU-27 
countries. It can be observed that while Belgium and the Czech Republic have the same 
diversity  of  specialisation  pattern  (0.83),  the  Czech  Republic,  with  fewer  trademark 
applications (3 482 compared to 7 694 for Belgium), has lower expected diversity for its 
size.  Accordingly,  the  Czech  Republic’s  diversity  index  (1.16)  is  larger  than  that  of 
Belgium (1.06) (Table 1). For the former country observed diversity is 16 % greater than 
expected, whereas for the latter country it is 6 % greater than expected. 

Focusing  on  EU-27  Member  States,  Slovakia,  Latvia  and  Slovenia  stand  apart  with  the 
highest diversity index for 2017–2019 (respectively, 1.24, 1.23, 1.22, Table 1). For their 
sizes  –  they  have  among  the  lowest  counts  of  trademark  applications  (respectively,  1 
230,  667  and  1  185  applications)  –  these  three  countries  thus  appear  to  have  been 
rather  successful  at  diversifying  the  thematic  breadth  of  the  trademark  applications 
forging their respective economy. They also stand out among those Member States with 
the highest growth rate in their diversity index (CAGR of, respectively, 1.9 %, 2.1 % and 
1.8 %). 

Table  2  illustrates,  for  each  country  (or  column),  trends  in  the  SI  across  Nice  classes. 
Each  delimited  cell  within  the  matrix  presents  the  trend  for  a  given  country  and  class 
using three-year running periods (from 2013–2015 to 2017–2019). The SI in each period 
is represented by a colour, with darker colours representing a greater departure from the 
world  level  (red  =  below  world  level;  green  =  above  world  level;  white  =  world  level). 
Darker  colours  across  classes  for  a  given  country  (red  and  green)  indicate  stronger 
polarity in its specialisation pattern (i.e. more specialisation), while lighter colours (closer 
to  white)  indicate  less  polarity  (i.e.  more  diversity).  As  shown  in  that  table,  larger 
countries such as Germany, France and Italy have fewer darker colours (both in the red 
and  green),  pointing  to  more  diversity  or  less  polarised  specialisation.  On  the  contrary, 
smaller  countries  such  as  Malta  and  Romania  generally  have  more  polarised 
specialisation patterns, pointing to less diversity. 

Latvia  is  mostly  specialised  in  services  (i.e.  classes  35  to  45).  It  is  also  specialised  in 
several  classes  for  goods  that  appear  to  be  thematically  close  to  one  another  –  that  is, 
classes  29,  30,  32  and  33  related  to  food  and  beverages.  The  observed  diversity  in 
Latvia’s trademark portfolio therefore appears to have remained focused in line with the 
smart specialisation concept (i.e. building and diversifying relying on existing strengths in 
related  areas).  Lithuania,  Latvia’s  neighbour,  has  a  similar  specialisation  pattern,  with 
some degree of emphasis on services as well as food and beverages. Along with Croatia 
and the Czech Republic, Lithuania also did well on the diversity index, with a score (1.16, 
Table 1) not too far below those of the leading Member States. Out of the three countries 
in this group, Lithuania is the only one to have experienced growth in its diversity index 
(3.5 %, Table 1), the strongest among all Member States. Slovakia and Slovenia are also 
specialised in many classes related to services. 

Three  EU-27 Member States stood out for the  thematic diversity of their trademark 
portfolio,  accounting  for  their  size:  Slovakia,  Latvia  and  Slovenia.  They  also  stood 
out  for  growing  that  diversity  from  2010–2012  to  2017–2019.  Lithuania,  Latvia’s 
neighbour, is also worthy of mention for its good diversity index combined with the 
strongest  observed  growth.  Each  of  these  four  countries  appear  to  specialise  in 
multiple  areas  that  are  thematically  related  (i.e.  close  to  one  another;  either  food 
and beverages  or services), leading to  IP portfolios that are  somewhat thematically 
homogeneous.  The  diversity  observed  in  their  trademark  portfolio  thus  seems  self-
coherent  and  in  line  with  the  smart  specialisation  concept  (i.e.  building  and 
diversifying relying on existing strengths). 

Among Member States, the lowest diversity index was observed for  Malta (0.64), which 
also has one of the smallest IP portfolios (1 835 trademark applications). The next three 
countries  with  the  lowest  diversity  index  were  Cyprus  (score  of  0.91  with  2  088 
trademarks),  Spain  (0.91;  29  420)  and  Italy  (0.92;  36  160)  (Table  1).  None  of  them 

11 

 
 
experienced  an  increase  in  diversity.  In  fact,  Malta  experienced  the  strongest  decrease 
(CAGR of -4.6 %). 

Even though the diversity index partly corrects for the overrepresentation of trademarks 
originating from European countries in the EUIPO, a home-bias advantage might persist 
for  European  countries/groupings.  This  would  be  the  case  if  the  organisations  from  the 
selected  international  comparators  that  applied  for  trademarks  at  the  EUIPO  are  not 
representative of these countries’ full populations of organisations (those for which IP is 
relevant).  It  would  appear  plausible  that  fewer  of  their  organisations  for  which  IP  is 
relevant,  most  probably  the  largest  ones,  would  apply  for  protection  in  foreign  markets 
such  as  Europe,  possibly  lowering  the  apparent  diversity  of  international  comparators. 
The  results  presented  in  Table  1  appear  to  support  this  hypothesis,  as  out  of  10 
international comparators (excluding the United Kingdom, which is in Europe), 60 % are 
below the diversity expected per their size, whereas this is only the case for roughly one 
in five Member States. 

Given the apparent home-bias advantage that EUIPO data seem to confer to Member 
States,  it  does  not  appear  suitable  to  compare  the  thematic  diversity  of  the 
trademark portfolios of European countries against international comparators. Future 
studies could thus investigate the potential usefulness of WIPO data in resolving this 
issue. 

With this potential limitation in mind, note that two international comparators scored well 
above expectations and above  many European countries: the Russian Federation, which 
is  geographically  close  to  Europe  and  had  a  diversity  index  of  1.22  (no  growth),  and 
Australia, with a score of 1.13 (no growth). Among international comparators, the lowest 
diversity index scores were observed for China (0.62) and South Africa (0.69). 

As  noted  above,  Table  2  provides  a  more  detailed  perspective  of  each  country’s  (or 
country grouping’s) SI over time and across Nice Classification headings. This perspective 
shows  the  tendency,  among  many  EU-27  countries,  for  a  higher  than  world  average 
specialisation  in  classes  29–33,  which  relate  to  foods  and  beverages.  Conversely,  the 
tendency  especially  among  EU-27  countries  is  for  a  lower  than  world  average 
specialisation  in  classifications  6–15,  which  span  several  areas  from  metals,  machinery, 
firearms  and  medical  instruments.  Additionally,  Malta  can  be  seen  to  be  highly 
specialised in a very small number of classes: 9, 28, 36, 38 and 41. As a result, it sits far 
below the world-level reference for specialisation in all other classes. This is reflected by 
the low diversity of its specialisation pattern and its low diversity index (Table 1). Most of 
the  selected  Asian  countries  show  strong  specialisation  in  classes  pertaining  to  goods 
(except foods and beverages), with scores well below the world level of specialisation in 
classes pertaining to services. 

12 

 
 
Table 1 

Diversity  in  the  specialisation  pattern  and  diversity  index  of  EU 
countries and international comparators based on the distribution, 
relative  to  the  world,  of  their  trademark  applications  across  Nice 
classes, 2010–2019 

Overseas territories for the Netherlands and the United Kingdom (including Gibraltar) are not included in the totals 
Note: 
for these countries. *Trends are presented using three-year running periods. The average yearly change in the scores for the 
three-year running periods is quantified by the CAGR computed between the starting (2010–2012) and ending (2017–2019) 
periods. The diversity index is coloured from red (below expectations) to green (above expectations), with white representing 
the world level. Negative CAGRs are in red and positive ones are in green. The scale is not the same across countries for the 
trend column. 
Source:  Produced by Science-Metrix using data from the EUIPO database  

13 

CountryTrademarks2017–19Diversity2017–19Score2017–19CAGR*(2017–19 vs 2010–12)Trend*(2010–12 to 2017–19)WO446,1081.001.000.0%EU-27255,9040.930.93-0.3%ERA309,4010.950.95-0.3%BE7,6940.831.060.3%BG2,1340.741.090.8%CZ3,4820.831.160.1%DK5,6940.861.13-0.2%DE64,7080.890.94-0.2%EE1,7260.711.07-1.5%EL2,8250.711.01-0.5%ES29,4200.800.91-0.1%FR25,9940.871.00-0.5%HR5670.681.17-0.8%IE3,3980.771.08-0.2%IT36,1600.830.92-0.4%CY2,0880.620.91-0.7%LV6670.731.232.1%LT1,3330.751.163.5%LU3,4490.761.06-0.2%HU1,9450.781.15-0.3%MT1,8350.430.64-4.6%NL14,4900.851.030.5%AT10,3890.871.080.0%PL10,7670.821.020.1%PT5,1990.720.96-0.7%RO2,4640.741.071.6%SI1,1850.781.221.8%SK1,2300.791.241.9%FI4,8770.801.08-0.1%SE10,1820.881.100.1%UK32,7110.880.990.3%BR8140.621.020.4%CA4,4600.761.030.0%CN40,4420.560.62-3.9%IN1,0490.600.97-2.2%JP8,5850.700.881.4%KR4,8100.640.860.4%RU1,4640.791.220.6%ZA5570.400.69-1.3%US52,0880.810.870.0%AU4,5300.831.130.1%Diversity index 
 
 
Table 2 

Trends  in  specialisation  pattern,  across  Nice  classes  (trademark  applications),  of  EU  countries  relative  to 
international comparators, 2013–2019 

Note: 

Overseas territories for the Netherlands and the United Kingdom (including Gibraltar) are not included in the totals for these countries. For a full list of class definitions see Appendix A – Nice 

Classification headings. Each delimited cell within the matrix presents a trend using three-year running periods (from 2013–2015 to 2017–2019). The specialisation index in each period is colour coded, with 
darker colours representing a greater departure from the world level (red = below world level; green = above world level; white = world level). Darker colours across classes for a given country (red and 
green) indicate stronger polarity in its specialisation pattern (i.e. more specialisation), and lighter colours (closer to white) indicate less polarity (i.e. more diversity). 
Source:  Produced by Science-Metrix using data from the EUIPO database 

14 

Class/CO123456789101112131415161718192021222324252627282930313233343536373839404142434445ITEU-27ERABEBGCZDKDEEEELESFRHRIELVLTLUHUMTUSAUBRCACNINJPKRRUZASKFISEUKNLATPLPTROSICY 
 
3.3  Results for design applications 

Table 3 shows diversity metrics related to community design applications at the country 
level as well as for country groupings in Europe (EU-27 and ERA). As described in Section 
3.1 for trademarks, the home bias in representation of European countries in EUIPO data 
suggests the EU-27 and ERA should not be compared to international comparators. Also 
given their size, they should be compared to individual Member States using the diversity 
index only. 

Using the diversity index, which accounts for the size of a country’s IP portfolio, some of 
the stand-out EU-27 Member States based on trademark applications again stood out for 
community  design  applications  for  the  2017–2019  period.  These  countries  include 
Slovenia  (diversity  index  of  1.64;  385  applications),  Latvia  (1.38;  189)  and  Lithuania 
(1.25; 274). As was the case for trademarks, both Slovenia (CAGR of 2.1 %) and Latvia 
(6 %) experienced noticeable growth in their diversity index moving from 2010–2012 to 
2017–2019. Other countries who registered a high diversity index combined with strong 
growth include Cyprus (diversity index of 1.26; CAGR of 14.5 %), Finland (1.19; 4.7 %) 
and Austria (1.19; 2.1 %). 

Croatia  had  a  very  high  diversity  index  in  2017–2019  (1.40),  but  it  has  been  declining 
since  2010–2012  (-6.5  %).  Among  the  three  largest  Member  States  by  number  of 
applications,  both  Germany  and  France  presented  with  observed  diversity  above 
expectation  (i.e.  diversity  index  >  1).  For  Germany,  this  meant  a  diversity  index  12  % 
above  expectation,  with  the  largest  diversity  of  specialisation  pattern  (0.82).  Both 
Germany  and  France  had  a  rather  stable  diversity  index.  The  third  largest  country  by 
number of applications, Italy, scored as expected, also with a stable score. 

Six  EU-27  Member  States  stood  out  for  the  thematic  diversity  of  their  community 
design portfolio, accounting for their size: Slovenia, Latvia, Cyprus, Lithuania, Finland 
and Austria. Apart from Lithuania, they also stood out for growing that diversity from 
2010–2012 to 2017–2019. Of these six countries, Slovenia, Latvia and Lithuania also 
stood out for their diversity index based on trademark applications. 

Among Member States, the lowest diversity index, by a wide margin, was again observed 
for  Malta  (0.42),  which  also  had  one  of  the  smallest  IP  portfolios  (356  design 
applications),  although  not  as  small  as  most  of  the  high-performing  Member  States  on 
this  dimension.  The  next  five  countries  with  the  lowest  diversity  index  all  had  similar 
scores. They included Romania, Greece, Luxembourg, Hungary and Bulgaria, with scores 
ranging  from  0.72  to  0.79  (output  size  ranging  from  526  to  1  698).  Of  those,  only 
Bulgaria increased its diversity index from 2010–2012 to 2017–2019. 

for 

the  selected 

trademark  applications, 

international 
As  previously  explained 
comparators  may  tend  towards  lower  diversity  than  European  countries  since  fewer  of 
their  organisations  (i.e.  those  for  which  IP  is  relevant)  may  apply  for  protection  in 
Europe.  It  is  possible  that  only  their  largest  applicants  would  seek  protection  in  foreign 
markets  such  as  Europe,  possibly  lowering  the  apparent  diversity  of  their  IP  portfolios. 
The results presented in Table 3 still support this hypothesis, but to a lesser extent than 
Table  1  for  trademark  applications.  For  instance,  out  of  10  international  comparators 
(excluding  the  United  Kingdom,  which  is  in  Europe),  50  %  were  below  the  diversity 
expected  per  their  size,  against  41  %  for  Member  States.  It  thus  appears  more 
reasonable  to  compare  the  diversity  index  of  international  comparators  to  those  of 
Member States for design than it is for trademark applications. Still we would recommend 
looking at WIPO data in the future to avoid this bias. 

While  most  international  comparators  scored  close  to  their  expected  level  of  diversity 
(diversity  index  ranging  from  0.90  to  1.10),  some  scored  well  above  expectations  and 
well  above  many  EU-27  Member  States.  They  include  the  Russian  Federation,  which  is 
geographically close to  Europe, with a diversity index of 1.35 (no growth) and Australia 

15 

 
 
with a score of 1.17. The diversity index was near expectation for the United States and 
China,  which  were  by  far  the  two  largest  international  comparators  per  their  number  of 
applications. This placed them just slightly below the aggregate scores for the EU-27 and 
ERA. For design applications, the United Kingdom, one of the largest European countries 
in the ERA, scored higher than any of the largest Member States (i.e. Germany, Italy and 
France), with a diversity index 20 % above expectations. This contrasts sharply with the 
United Kingdom’s diversity index for trademarks, which was just as expected for the size 
of its IP portfolio (0.99). 

Table 4 illustrates, for each country (or column), trends in the SI across Locarno classes. 
The specialisation patterns of countries based on design applications are  very polarised, 
with very high and low specialisation scores (darker colours showcasing a lower degree of 
diversity  in  absolute  terms)  for  most  countries.  As  a  result,  there  are  no  trends  in  the 
specialisation  patterns  of  EU-27  countries  based  on  design  applications.  If  anything, 
there appear to be more Member States specialised in class 9 (packages and containers 
for  the  transport  and  handling  of  goods),  class  20  (sales  and  advertising  equipment, 
signs) and class 32 (graphic symbols and logos, surface patterns, ornamentation) than in 
any other class. 

16 

 
Table 3 

Diversity 
in  the  specialisation  pattern  of  EU  countries  and 
comparators  based  on  the  distribution,  relative  to  the  world,  of 
their community design applications across Locarno classes, 2010–
2019 

Note: 
Overseas territories for the Netherlands and the United Kingdom (including Gibraltar) are not included in the totals 
for these countries. *Trends are presented using three-year running periods. The average yearly change in the scores for the 
three-year running periods is quantified by the CAGR computed between the starting (2010–2012) and ending (2017–2019) 
periods. The diversity index is coloured from red (below expectations) to green (above expectations), with white representing 
the world level. Negative CAGRs are in red and positive ones are in green. †The CAGR for Croatia compares 2017–2019 to 
2011–2013. The scale is not the same across countries for the trend column. 
Source:  Produced by Science-Metrix using data from the EUIPO database  

17 

CountryDesigns2017–19Diversity2017–19Score2017–19CAGR*(2017–19 vs 2010–12)Trend*(2010–12 to 2017–19)WO277,2951.001.000.0%EU-27167,2980.861.040.1%ERA194,6550.891.060.3%BE3,2260.601.170.5%BG1,6980.360.793.2%CZ2,9830.440.87-4.4%DK4,9230.500.91-4.5%DE54,0140.821.120.6%EE5240.421.15-4.3%EL5260.270.73-5.2%ES9,9960.711.170.5%FR17,2050.671.040.7%HR1700.381.40-6.5%IE1,2290.410.95-4.5%IT29,5500.691.000.9%CY1730.351.2614.5%LV1890.391.386.0%LT2740.391.251.0%LU9910.310.74-1.2%HU6630.290.75-2.6%MT3560.140.42-20.3%NL8,5160.550.93-3.6%AT6,4130.671.192.1%PL12,0780.580.93-0.8%PT2,4250.511.044.0%RO1,3230.320.720.6%SI3850.561.642.1%SK5540.401.090.9%FI2,1890.571.194.7%SE4,7260.591.10-0.7%UK17,2030.771.201.6%BR4640.340.962.8%CA1,5200.481.070.4%CN30,1230.660.960.4%IN3790.351.041.8%JP7,2770.350.60-1.9%KR1,7690.491.069.7%RU1900.381.352.1%ZA2020.260.91-7.6%US31,8030.670.97-0.5%AU1,4860.521.17-0.7%Diversity index 
 
Table 4 

Trends in specialisation pattern, across Locarno classes (community design applications), of EU countries relative to 
international comparators, 2013–2019 

Note: 

Overseas territories for the Netherlands and the United Kingdom (including Gibraltar) are not included in the totals for these countries. For a full list of class definitions see Appendix B – Locarno 

Classification headings. Each delimited cell within the matrix presents a trend using three-year running periods (from 2013–2015 to 2017–2019). The specialisation index in each period is colour coded, with 
darker colours representing a greater departure from the world level (red = below world level; green = above world level; white = world level). Darker colours across classes for a given country (red and 
green) indicate stronger polarity in its specialisation pattern (i.e. more specialisation), and lighter colours (closer to white) indicate less polarity (i.e. more diversity). 
Source:  Produced by Science-Metrix using data from the EUIPO database 

18 

Class/CO1234567891011121314151617181920212223242526272829303132EU-27ERABEBGCZDKDEEEMTELESFRHRIEITCYLVLTLUHUATPLPTROSIUSAUCNINJPKRRUZABRCASKFISEUKNL 
 
 
4 

Policy brief on EPO patent applications 

This  brief  on  patents  aimed  to  test  the  suitability  of  using  EPO  data  to  monitor  the 
impacts of investment directionality on the specialisation of Member States (and regional 
aggregates such as the EU-27 and ERA) in specific innovation areas. It also intended to 
test whether the specialisation (or diversity) of Member States (and regional aggregates 
such  as  the  EU-27  and  ERA)  could  adequately  be  benchmarked  against  international 
comparators  (i.e.  the  United  Kingdom,  Australia,  Brazil,  Canada,  China,  India,  Japan, 
South  Korea,  the  Russian  Federation,  South  Africa  and  the  United  States)  using  a 
regional data source (i.e. EPO). 

As  was  observed  for  trademarks  and  designs  (Section  3),  there  is  a  logarithmic 
relationship between a country’s number of EPO patent applications and the diversity of 
its  specialisation  pattern  (Figure  2).  Accordingly,  individual  countries  should  still  be 
compared relying on the diversity index or, alternatively, directly comparing the diversity 
of specialisation patterns across countries of a similar size. 

Figure 2 

Relationship  between  a  country’s  number  of  IP  applications  and 
the diversity of its specialisation pattern for patents (2015–2017) 

Note: 

A country’s diversity of specialisation pattern was characterised using NACE sectors. The countries used in this 

analysis include ERA countries and the selected international comparators. 
Source:  Produced by Science-Metrix using data from the EUIPO and PATSTAT databases 

4.1  Results for patents 

Table 5 shows diversity metrics related to patent applications at the country level as well 
as  for  country  groupings  in  Europe  (EU-27  and  ERA).  Again,  due  to  a  home-bias 
advantage in representation of Member States in EPO data, the EU-27 and ERA should 
not be compared to international comparators. Also, given their size, they should 
be compared to individual Member States using the diversity index only. 

Looking within EU-27 countries, and accounting for the size of their output (by number of 
applications),  the  Member  States  with  the  most  thematically  diverse  patent  portfolios 
included the Czech Republic (diversity index of 1.18), Lithuania (1.17), Hungary (1.15), 
Bulgaria (1.13), Poland (1.13) and Slovakia (1.13) in 2015–2017. Bulgaria and Lithuania 
also  stood  out  for  the  growth  of  their  diversity  index  from  2010–2012  to  2015–2017 
(CAGR of, respectively, 2 % and 3.2 %), as did Luxembourg (3.2 %), Estonia (3 %) and 
Latvia (2.3 %) (Table 5). Lithuania also stood out for its diversity index on the other two 
types  of  IP  reported  here  (trademarks  and  designs).  Slovakia  did  so  as  well  for 
trademarks.  Latvia  also  stood  out  for  both  the  score  and  growth  of  its  diversity  index 
based on trademarks and designs. 

19 

020K40K60K80K100K120KNon-transformedy = 0.04ln(x) + 0.34R² = 0.60110KLogtransformed 
 
Six EU-27 Member States stood out for the thematic diversity of their patent portfolio, 
accounting  for  their  size:  the  Czech  Republic,  Lithuania,  Hungary,  Bulgaria,  Poland 
and  Slovakia.  Bulgaria  and  Lithuania  also  stood  out  for  the  growth  of  their  diversity 
indexes, as did Luxembourg, Estonia and Latvia. 

Among the three largest Member States by number  of applications, France  exhibited an 
observed diversity 10 % above expectation (diversity index of 1.10). Germany and Italy 
were  both  near  expectation  for  the  size  of  their  IP  portfolios  (i.e.  diversity  index  near 
1.00). All three countries had rather stable diversity indexes over the studied period. As 
for trademarks and designs, Malta scored the smallest diversity index (0.72) for patents. 
Other  countries  with  less  diversity  than  expected  for  patents  include  Cyprus  (0.81)  and 
Finland  (0.85).  Of  the  three,  Finland  was  the  only  country  with  at  least  a  few  hundred 
patents in 2017–2019. 

The  results  presented  in  Table  5  show  that  out  of  10  international  comparators 
(excluding  the  United  Kingdom,  which  is  in  Europe),  50  %  were  below  the  diversity 
expected per their size, against 44 % for Member States. The difference is not huge but 
could  be  explained  by  the  home-bias  in  favour  of  European  countries.  The  selected 
international  comparators  may  tend  towards  lower  diversity  than  European  countries 
because  fewer  of  their  organisations  (i.e.  those  for  which  IP  is  relevant)  may  apply  for 
protection  in  Europe.  It  is  possible  that  only  their  largest  applicants  would  seek 
protection in foreign markets such as Europe, possibly lowering the apparent diversity of 
their IP portfolios. Future studies investigating the specialisation patterns of countries on 
an  international  scale  should  consider  using  data  on  PCT  applications  to  minimise  the 
home-bias advantage. 

Among  international  comparators,  Brazil  and  Canada  both  stood  out  for  their  high 
diversity  index  (respectively  1.25  and  1.18).  Only  for  Canada  did  the  diversity  index 
experience a notable increase among international comparators (CAGR of 2.6 %). As for 
design applications, the diversity index was near expectation for the United States, Japan 
and China, the three largest international comparators per their size.  Thus, their scores 
were just slightly below the aggregate scores for the EU-27 and ERA. Another similarity 
with design applications lies in the fact that the United Kingdom scored higher than most 
of  the  largest  Member  States  (i.e.  Germany,  Italy),  with  a  diversity  index  11  %  above 
expectations. It scored similarly to France (1.10). 

Table 6 provides a detailed perspective of each country’s SI over time and across NACE 
sectors.  This  perspective  shows  the  tendency,  especially  among  EU-27  countries,  for  a 
higher  than  world  average  specialisation  in  sectors  41–43,  which  relate  to  construction. 
Conversely,  the  tendency,  especially  among  EU-27  countries,  is  for  a  lower  than  world 
average  specialisation  in  sectors  19  and  58–63,  which  respectively  cover  the 
manufacturing of fuels and information and communication services. 

20 

 
 
Table 5 

Diversity 
in  the  specialisation  pattern  of  EU  countries  and 
comparators  based  on  the  distribution,  relative  to  the  world,  of 
their EPO patent applications across NACE sectors, 2010–2017 

Note: 
Overseas territories for the Netherlands and the United Kingdom (including Gibraltar) are not included in the totals 
for these countries. *Trends are presented using three-year running periods. The average yearly change in the scores for the 
three-year running periods is quantified by the CAGR computed between the starting (2010–2012) and ending (2015–2017) 
periods. The diversity index is coloured from red (below expectations) to green (above expectations), with white representing 
the world level. Negative CAGRs are in red and positive ones are in green. The scale is not the same across countries for the 
trend column. 
Source:  Produced by Science-Metrix using data from the EUIPO database   

21 

CountryPatents2015–17Score2015–17Score2015–17CAGR*(2015–17 vs 2010–12)Trend*(2010–12 to 2015–17)WO433,8561.001.000.0%EU-27160,6120.871.030.4%ERA197,0960.891.040.4%BE5,0380.741.050.6%BG980.601.132.0%CZ8080.741.180.7%DK4,1690.680.980.3%DE66,7350.790.980.0%EE1170.601.113.0%EL2880.641.111.1%ES4,9330.771.100.1%FR28,2370.841.10-0.4%HR720.510.97-1.0%IE1,2370.570.89-3.0%IT13,7600.741.00-0.4%CY360.400.81NALV550.541.062.3%LT820.621.173.2%LU2900.530.913.2%HU5810.701.150.7%MT460.360.72NANL11,6500.680.93-1.1%AT6,2050.700.99-0.8%PL1,7430.741.131.2%PT5630.611.011.2%RO3320.560.95-4.9%SI3940.631.060.5%SK1850.641.131.3%FI4,0590.590.85-0.7%SE8,8970.710.98-0.7%UK17,8370.841.110.0%BR7900.781.25-0.4%CA5,7590.831.182.6%CN29,8420.730.95-0.7%IN3,8420.500.73-1.2%JP60,2110.760.950.0%KR20,0460.660.880.4%RU8450.681.09-0.1%ZA3600.591.00-2.9%US107,9670.800.960.9%AU2,7510.681.01-1.7%Diversity index 
 
Table 6 

Trends in specialisation pattern, across NACE sectors (patent applications), of EU countries relative to international 
comparators, 2013–2019 

Note: 

Overseas territories for the Netherlands and the United Kingdom (including Gibraltar) are not included in the totals for these countries. For a full list of NACE sector definitions see Appendix C – 

NACE Classification headings. Each delimited cell within the matrix presents a trend using three-year running periods (from 2011–2013 to 2015–2017). The specialisation index in each period is colour 
coded, with darker colours representing a greater departure from the world level (red = below world level; green = above world level; white = world level). Darker colours across classes for a given country 
(red and green) indicate stronger polarity in its specialisation pattern (i.e. more specialisation), and lighter colours (closer to white) indicate less polarity (i.e. more diversity). 
Source:  Produced by Science-Metrix using data from the PATSTAT database

22 

NACE sector/Country10-1213-1516-18 (incl. 18.2)192020 (without overlap with 21)21222324-25 (incl 25.4)2826272930 (incl. 30.1 & 30.3)31-33 (incl 32.5)41-4358-63ITEU-27ERABEBGCZDKDEEEELESFRHRIESISKFILVLTLUHUNLATUSAUCNINJPKRRUZABRCASEUKPLPTRO 
 
5 

Conclusion 

This report describes the results of two policy briefs analysing the specialisation patterns 
of  countries  from  a  diversity  perspective  using  data  from  EUIPO  (for  trademarks  and 
designs,  first  brief)  and  the  EPO  (for  patents,  second  brief).  They  follow  up  on  two 
analytical  reports  (one  on  trademarks  and  designs  and  the  other  one  on  patents) 
produced as part of the KIRI project. While these reports briefly introduced the analysis 
of  specialisation  at  country  level  in  the  ERA,  they  did  not  assess  their  usability  in 
assessing  the  potential  impacts  of  investment  directionality,  an  aspect  that  will  be 
important in the monitoring context for the new ERA (2021–2027). 

The  analyses  presented  in  these  briefs  thus  intended  to  address  a  few  questions  whose 
responses  were  then  used  in  formulating  recommendations  to  guide  future  monitoring 
efforts in relation to the impacts of the smart directionality of R&I investments under the 
new  ERA. For  example, should countries be ranked based on the SI?  If so, should it be 
within  certain  size  limits?  Should  the  SI  be  strictly  used  to  follow  the  progress  of 
individual  countries  relative  to  their  respective  baseline  value  and  target?  Can  regional 
databases  be  used  in  international  comparisons  (e.g.  EUIPO)?  The  policy  briefs  also 
aimed  to  extract  general  tendencies  in  the  specialisation  patterns  of  the  selected 
countries. 

The analyses covered all Member States of the EU-27, key international comparators that 
were  part  of  the  EU’s  International  Strategy  Roadmap  (Brazil,  Canada,  China,  India, 
Japan, South Korea, the Russian Federation, South Africa and the United States), and the 
United  Kingdom  and  Australia.  At  the  aggregate  level,  two  European  groupings  were 
included: the EU-27 and the ERA. 

KEY FINDINGS AND RECOMMENDATIONS FOR FUTURE WORK 

The  results  showed  that  the  diversity  of  a  country’s  specialisation  pattern  is  dependent 
on  the  size  of  its  output  for  trademarks,  designs  and  patents.  The  relationship  between 
output size and diversity is logarithmic, and its non-linear character should be accounted 
for  in  comparing  diversity  across  countries  of  different  sizes.  In  fact,  we  proposed  a 
diversity  index  to  enable  comparisons  across  countries  of  different  sizes.  In  all  cases, 
we  recommend  not  comparing  the  raw  diversity  (or  specialisation  in  specific 
areas) across countries of different sizes. 

It  should  be  noted  that,  contrary  to  raw  output  sizes,  normalising  the  SI  by  population 
size  to  account  for  country  size  differences  would  drastically  disadvantage  larger 
countries.  Smaller  countries  scored  more  extreme  values  and  would  be  normalised  by 
smaller  denominators11.  Plus,  the  large  scores  of  smaller  countries  can  more  easily 
fluctuate  year-on-year.  For  this  reason,  it  is  best  to  compare  the  SI,  in  a  specific  area, 
among countries of a similar size. 

Using trademarks as an example, Latvia, Lithuania, Slovakia and Slovenia were mostly in 
the lower half of EU-27 countries for raw diversity and output size but had some of the 
highest  scores  based  on  the  diversity  index.  The  diversity  in  their  portfolio  related  to 
many classes in the food and beverages as well as services. This appeared to be in line 
with  the  smart  specialisation  concept  (i.e.  building  and  diversifying  relying  on  existing 
strengths  or  diversifying  in  related  areas).  Given  their  size,  they  naturally  could  not  be 
present  everywhere.  Accordingly,  the  raw  diversity  of  their  specialisation  patterns  was 
more  polarised  than  that  of  larger  countries  (e.g.  Germany,  France  and  Italy),  with 
strong and weak scores across areas. 

11  For  the  diversity  of  specialisation  patterns,  the  opposite  was  observed:  larger  countries  scored 
higher  than  smaller  ones.  Here  again,  normalisation  by  population  size  is  not  possible  since 
diversity is bounded between 0 and 1. Here again, larger countries would be disadvantaged. 

23 

 
                                                 
Seeing these patterns, it would appear unfair to set a unique target, for the SI, 
in monitoring the impacts of smart directionality policies. Smaller countries exhibit 
more polarised specialisation patterns in part because they must rely on their accessible 
pool  of  expertise,  which  is  naturally less  diversified  than  for  larger  countries.  In  turn,  a 
small country already specialised in the digital sciences would be advantaged relative to a 
country  of  a  similar  size  currently  lacking  the  necessary  expertise  to  increase  its 
specialisation in that area. Accordingly, it seems that at a minimum, different targets 
should be used in assessing the performance of each country according to their 
baseline  value.  In  fact,  the  SI  is  probably  best  suited  in  following  the  performance  of 
individual  countries  relative  to  their  respective  baseline  and  target,  rather  than  in 
benchmarking  countries  having  different  national  contexts  (e.g.  comparing  two  small 
countries,  one  already  with  a  strength  in  the  target  area  and  one  without  such  initial 
capacity). 

Finally,  it  was  found  that  the  EUIPO  and  EPO  data  used  for  producing  the  trademark, 
design  and  patent  indicators  presented  a  strong  enough  bias  towards  the  European 
market to favour Member States (as well as EU-27/ERA) at the expense of international 
comparators  of  a  similar  size.  To  enable  international  comparisons,  future  studies 
should consider using alternative data sources such as the WIPO. 

24 

 
 
 
APPENDIX A 

Class 

Description 

Class 1 

Class 2 

Class 3 

Class 4 

Class 5 

Class 6 

Class 7 

Class 8 

Class 9 

Class 10 

Class 11 

Class 12 

Class 13 

Class 14 

Class 15 

Chemicals for use in industry, science and photography, as well as in 
agriculture, horticulture and forestry; Unprocessed artificial resins, 
unprocessed plastics; Fire extinguishing and fire prevention compositions; 
Tempering and soldering preparations; Substances for tanning animal skins 
and hides; Adhesives for use in industry; Putties and other paste fillers; 
Compost, manures, fertilizers; Biological preparations for use in industry and 
science 

Paints, varnishes, lacquers; Preservatives against rust and against 
deterioration of wood; Colorants, dyes; Inks for printing, marking and 
engraving; Raw natural resins; Metals in foil and powder form for use in 
painting, decorating, printing and art 

Non-medicated cosmetics and toiletry preparations; Non-medicated 
dentifrices; Perfumery, essential oils; Bleaching preparations and other 
substances for laundry use; Cleaning, polishing, scouring and abrasive 
preparations 
Industrial oils and greases, wax; Lubricants; Dust absorbing, wetting and 
binding compositions; Fuels and illuminants; Candles and wicks for lighting 
Pharmaceuticals, medical and veterinary preparations; Sanitary preparations 
for medical purposes; Dietetic food and substances adapted for medical or 
veterinary use, food for babies; Dietary supplements for humans and 
animals; Plasters, materials for dressings; Material for stopping teeth, dental 
wax; Disinfectants; Preparations for destroying vermin; Fungicides, 
herbicides 

Common metals and their alloys, ores; Metal materials for building and 
construction; Transportable buildings of metal; Non-electric cables and wires 
of common metal; Small items of metal hardware; Metal containers for 
storage or transport; Safes 

Machines, machine tools, power-operated tools; Motors and engines, except 
for land vehicles; Machine coupling and transmission components, except for 
land vehicles; Agricultural implements, other than hand-operated hand tools; 
Incubators for eggs; Automatic vending machines 

Hand tools and implements, hand-operated; Cutlery; Side arms, except 
firearms; Razors 

Scientific, nautical, surveying, photographic, cinematographic, optical, 
weighing, measuring, signalling, checking (supervision), life-saving and 
teaching apparatus and instruments; Apparatus and instruments for 
conducting, switching, transforming, accumulating, regulating or controlling 
electricity; Apparatus for recording, transmission or reproduction of sound or 
images; Magnetic data carriers, recording discs; Compact discs, DVDs and 
other digital recording media; Mechanisms for coin-operated apparatus; Cash 
registers, calculating machines, data processing equipment, computers; 
Computer software; Fire-extinguishing apparatus 

Surgical, medical, dental and veterinary apparatus and instruments; Artificial 
limbs, eyes and teeth; Orthopedic articles; Suture materials; Therapeutic 
and assistive devices adapted for the disabled; Massage apparatus; 
Apparatus, devices and articles for nursing infants; Sexual activity 
apparatus, devices and articles 
Apparatus for lighting, heating, steam generating, cooking, refrigerating, 
drying, ventilating, water supply and sanitary purposes 

Vehicles; Apparatus for locomotion by land, air or water 

Firearms; Ammunition and projectiles; Explosives; Fireworks 

Precious metals and their alloys; Jewellery, precious and semi-precious 
stones; Horological and chronometric instruments 

Musical instruments 

25 

 
Class 16 

Class 17 

Class 18 

Class 19 

Class 20 

Class 21 

Class 22 

Class 23 

Class 24 

Class 25 

Class 26 

Class 27 

Class 28 

Class 29 

Class 30 

Class 31 

Class 32 

Class 33 

Class 34 

Class 35 

Class 36 

Class 37 

Paper and cardboard; Printed matter; Bookbinding material; Photographs; 
Stationery and office requisites, except furniture; Adhesives for stationery or 
household purposes; Drawing materials and materials for artists; 
Paintbrushes; Instructional and teaching materials; Plastic sheets, films and 
bags for wrapping and packaging; Printers' type, printing blocks 

Unprocessed and semi-processed rubber, gutta-percha, gum, asbestos, mica 
and substitutes for all these materials; Plastics and resins in extruded form 
for use in manufacture; Packing, stopping and insulating materials; Flexible 
pipes, tubes and hoses, not of metal 

Leather and imitations of leather; Animal skins and hides; Luggage and 
carrying bags; Umbrellas and parasols; Walking sticks; Whips, harness and 
saddlery; Collars, leashes and clothing for animals 
Building materials [non-metallic]; Non-metallic rigid pipes for building; 
Asphalt, pitch and bitumen; Non-metallic transportable buildings; 
Monuments, not of metal 
Furniture, mirrors, picture frames; Containers, not of metal, for storage or 
transport; Unworked or semi-worked bone, horn, whalebone or mother-of-
pearl; Shells; Meerschaum; Yellow amber 
Household or kitchen utensils and containers; Cookware and tableware, 
except forks, knives and spoons; Combs and sponges; Brushes, except 
paintbrushes; Brush-making materials; Articles for cleaning purposes; 
Unworked or semi-worked glass, except building glass; Glassware, porcelain 
and earthenware 

Ropes and string; Nets; Tents and tarpaulins; Awnings of textile or synthetic 
materials; Sails; Sacks for the transport and storage of materials in bulk; 
Padding, cushioning and stuffing materials, except of paper, cardboard, 
rubber or plastics; Raw fibrous textile materials and substitutes therefor 

Yarns and threads, for textile use 

Textiles and substitutes for textiles; Household linen; Curtains of textile or 
plastic 

Clothing, footwear, headgear 

Lace and embroidery, ribbons and braid; Buttons, hooks and eyes, pins and 
needles; Artificial flowers; Hair decorations; False hair 

Carpets, rugs, mats and matting, linoleum and other materials for covering 
existing floors; Wall hangings [non-textile] 

Games, toys and playthings; Video game apparatus; Gymnastic and sporting 
articles; Decorations for Christmas trees 

Meat, fish, poultry and game; Meat extracts; Preserved, frozen, dried and 
cooked fruits and vegetables; Jellies, jams, compotes; Eggs; Milk and milk 
products; Oils and fats for food 

Coffee, tea, cocoa and artificial coffee; Rice; Tapioca and sago; Flour and 
preparations made from cereals; Bread, pastries and confectionery; Edible 
ices; Sugar, honey, treacle; Yeast, baking-powder; Salt; Mustard; Vinegar, 
sauces [condiments]; Spices; Ice [frozen water] 

Raw and unprocessed agricultural, aquacultural, horticultural and forestry 
products; Raw and unprocessed grains and seeds; Fresh fruits and 
vegetables, fresh herbs; Natural plants and flowers; Bulbs, seedlings and 
seeds for planting; Live animals; Foodstuffs and beverages for animals; Malt 

Beers; Mineral and aerated waters and other non-alcoholic beverages; Fruit 
beverages and fruit juices; Syrups and other preparations for making 
beverages 

Alcoholic beverages [except beers] 

Tobacco; Smokers' articles; Matches 

Advertising; Business management; Business administration; Office functions 

Insurance; Financial affairs; Monetary affairs; Real estate affairs 

Building construction; Repair; Installation services 

26 

 
Class 38 

Class 39 

Class 40 

Class 41 

Class 42 

Class 43 

Class 44 

Class 45 

Telecommunications 

Transport; Packaging and storage of goods; Travel arrangement 

Treatment of materials 

Education; Providing of training; Entertainment; Sporting and cultural 
activities 

Scientific and technological services and research and design relating 
thereto; Industrial analysis and research services; Design and development 
of computer hardware and software 

Services for providing food and drink; Temporary accommodation 

Medical services; Veterinary services; Hygienic and beauty care for human 
beings or animals; Agriculture, horticulture and forestry services 

Legal services; Security services for the physical protection of tangible 
property and individuals; Personal and social services rendered by others to 
meet the needs of individuals 

APPENDIX B – LOCARNO CLASSIFICATION HEADINGS 

Class 

Description 

Class 1 

Foodstuffs 

Class 2 

Articles Of Clothing And Haberdashery 

Class 3 

Travel Goods, Cases, Parasols And Personal Belongings, Not Elsewhere Specified 

Class 4 

Brushware 

Class 5 

Textile Piecegoods, Artificial And Natural Sheet Material 

Class 6 

Furnishing 

Class 7 

Household Goods, Not Elsewhere Specified 

Class 8 

Tools And Hardware 

Class 9 

Packages And Containers For The Transport Or Handling Of Goods 

Class 10 

Clocks And Watches And Other Measuring Instruments, Checking And Signalling Instruments 

Class 11 

Articles Of Adornment 

Class 12  Means Of Transport Or Hoisting 

Class 13 

Equipment For Production, Distribution Or Transformation Of Electricity 

Class 14 

Recording, Communication Or Information Retrieval Equipment 

Class 15  Machines, Not Elsewhere Specified 

Class 16 

Photographic, Cinematographic And Optical Apparatus 

Class 17  Musical Instruments 

Class 18 

Printing And Office Machinery 

Class 19 

Stationery And Office Equipment, Artists’ And Teaching Materials 

Class 20 

Sales And Advertising Equipment, Signs 

Class 21 

Games, Toys, Tents And Sports Goods 

Class 22 

Arms, Pyrotechnic Articles, Articles For Hunting, Fishing And Pest Killing 

Class 23 

Fluid Distribution Equipment, Sanitary, Heating, Ventilation And Air-Conditioning Equipment, 
Solid Fuel 

Class 24  Medical And Laboratory Equipment 

Class 25 

Building Units And Construction Elements 

Class 26 

Lighting Apparatus 

Class 27 

Tobacco And Smokers’ Supplies 

27 

 
Class 28 

Pharmaceutical And Cosmetic Products, Toilet Articles And Apparatus 

Class 29 

Devices And Equipment Against Fire Hazards, For Accident Prevention And For Rescue 

Class 30 

Articles For The Care And Handling Of Animals 

Class 31  Machines And Appliances For Preparing Food Or Drink, Not Elsewhere Specified 

Class 32 

Graphic Symbols And Logos, Surface Patterns, Ornamentation 

APPENDIX C – NACE CLASSIFICATION HEADINGS 

NACE Rev 2.0 code 

Category 

Description 

10-12 

13-15 

Manufacturing 

Food, Beverages And Tobacco 

Manufacturing 

Textiles, Fur And Leather 

16-18 (incl. 18.2) 

Manufacturing 

Wood, Paper, Printing, Publishing 

19 

20 

20 (without overlap 
with 21) 

Manufacturing 

Coke, Refined Petroleum Products And Nuclear Fuel 

Manufacturing 

Chemicals And Chemical Products 

Manufacturing 

Chemicals And Chemical Products (Without Pharma) 

21 

22 

23 

Manufacturing 

Pharmaceuticals 

Manufacturing 

Rubber And Plastic Products 

Manufacturing 

Non-Metallic Mineral Products 

24-25 (incl 25.4) 

Manufacturing 

Basic Metals And Fabricated Metal Products 

28 

26 

27 

29 

Manufacturing 

Machinery And Equipment, N.E.C. 

Manufacturing 

Computer, Electronic And Optical Products 

Manufacturing 

Electrical Machinery And Apparatus N.E.C. 

Manufacturing 

Motor Vehicles, Trailers And Semi-Trailers 

30 (incl. 30.1 & 
30.3) 

Manufacturing 

Other Transport Equipment 

31-33 (incl. 32.5) 

Manufacturing 

Furniture, Other Manufacturing N.E.C 

41-43 

58-63 

Construction 

Construction 

Services 

Information And Communication 

28 

 
 
 
 
 
Getting in touch with the EU 

IN PERSON 
All over the European Union there are hundreds of Europe Direct information centres. 
You can find the address of the centre nearest you at: https://europa.eu/european-union/contact_en 

ON THE PHONE OR BY EMAIL 
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Finding information about the EU 

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The  Key  indicators  for  Research  and  Innovation  project  was  a  three-year  project 
designed  to  support  the  development  and  implementation  of  the  Commission's  R&I 
policies with evidence at the EU, country and regional levels. As a result, 11 publications 
reflecting the work of the project were produced: 

-  The  final  study  report  makes  a  summary  of  the  work  done  for  each  of  the  six  work 

packages of the project; 

-  The  final  study  approach  report  covers  the  methodology  applied  to  collect  and  treat 

the data and the computation of the indicators; 

-  The ten policy briefs provide an in-depth analysis of different research questions with 
the  aim  of  formulating  policy  recommendations  in  support  of  evidence-based 
policymaking. 

Studies and reports